Add comments.

paddle/api/PaddleAPI.h

@@ -799,22 +799,61 @@ class ParameterUpdater {
   static ParameterUpdater* createLocalUpdater(OptimizationConfig* config);
   ~ParameterUpdater();
 
+  /**
+   * @brief initialize Parameter Updater by GradientMachine.
+   * @param gm
+   */
   void init(const GradientMachine& gm);
 
+  /**
+   * @brief begin of a training/testing of one pass.
+   */
   void startPass();
 
+  /**
+   * @brief end of a traning/testing of one pass.
+   */
   void finishPass();
 
+  /**
+   * @brief begin of a training/testing of one batch.
+   * @param data batch's size
+   * @return PassType, mostly will be training.
+   */
   PassType startBatch(size_t batchSize);
 
+  /**
+   * @brief end of a traning/testing of one batch
+   * @param cost current batch cost.
+   */
   void finishBatch(float cost);
 
+  /**
+   * @brief update a parameter (by local optimizer or by cluster pserver)
+   * @param param
+   */
   void update(Parameter* param);
 
+  /**
+   * @brief restore the average parameter.
+   * @note It is only used in AverageOptimizer. Restore will get the current
+   * PARAMETER_VALUE back.
+   */
   void restore();
 
+  /**
+   * @brief apply. Store the average parameter.
+   * @note It is only used in AverageOptimizer. Apply will store the current
+   * PARAMETER_VALUE to buffer, calcaualte current Average Parameter, and save
+   * it to PARAMETER_VALUE.
+   */
   void apply();
 
+  /**
+   * @brief catchUpWith The Regularization will be delayed in many situations(
+   * pserver, local sparse). Catch Up means catch the regularization up, apply
+   * regularization to all params.
+   */
   void catchUpWith();
 
 private:
@@ -830,10 +869,21 @@ class Evaluator {
 public:
   ~Evaluator();
 
+  /**
+   * @brief begin an evaluate stage.
+   */
   void start();
 
+  /**
+   * @brief end an evaluate stage.
+   */
   void finish();
 
+  /**
+   * @brief toString will get a evaluate result.
+   *
+   * __repr__ method in python
+   */
   std::string toString();
 
 private: